Salonen JT. Socioeconomic status and risk of cancer, cerebral stroke and death
due to coronary heart disease and any disease: a longitudinal study in eastern
Finland. J Epidemiol Community Health.1982;36:294-297.Google Scholar

Context.— A prominent hypothesis regarding social inequalities in mortality is
that the elevated risk among the socioeconomically disadvantaged is largely
due to the higher prevalence of health risk behaviors among those with lower
levels of education and income.

Design.— Longitudinal survey study investigating the impact of education, income,
and health behaviors on the risk of dying within the next 7.5 years.

Participants.— A nationally representative sample of 3617 adult women and men participating
in the Americans' Changing Lives survey.

Main Outcome Measure.— All-cause mortality verified through the National Death Index and death
certificate reviews.

Results.— Educational differences in mortality were explained in full by the strong
association between education and income. Controlling for age, sex, race,
urbanicity, and education, the hazard rate ratio of mortality was 3.22 (95%
confidence interval [CI], 2.01-5.16) for those in the lowest-income group
and 2.34 (95% CI, 1.49-3.67) for those in the middle-income group. When health
risk behaviors were considered, the risk of dying was still significantly
elevated for the lowest-income group (hazard rate ratio, 2.77; 95% CI, 1.74-4.42)
and the middle-income group (hazard rate ratio, 2.14; 95% CI, 1.38-3.25).

Conclusion.— Although reducing the prevalence of health risk behaviors in low-income
populations is an important public health goal, socioeconomic differences
in mortality are due to a wider array of factors and, therefore, would persist
even with improved health behaviors among the disadvantaged.

OVER THE PAST several decades, health behavior or lifestyle factors—smoking
cigarettes, being overweight, drinking alcoholic beverages, and being physically
inactive or leading a sedentary lifestyle—have often been cited as the
major determinants of premature and preventable morbidity and mortality.1-7
More recently, differences in health outcomes by socioeconomic position have
been recognized as a persisting and perhaps even increasing public health
problem.8-12
Less well understood, however, is the relationship between health risk behaviors
and socioeconomic differentials in health, especially in nationally representative
samples. In a number of longitudinal studies, important socioeconomic indicators—such
as income and education—have been shown to be inversely associated with
various mortality outcomes, including premature mortality, cardiovascular
mortality, and death from all causes.13-18
In addition, it is well documented that people of lower socioeconomic position
are significantly more likely to lead a sedentary lifestyle, to be overweight,
and to smoke cigarettes.19-22
Thus, a prominent hypothesis is that the elevated mortality risk associated
with low levels of income and education is primarily due to the higher prevalence
of health risk behaviors among people who are poor and/or have low educational
attainment.3,23-25
However, previous efforts to explain socioeconomic differences in mortality
in a variety of subpopulations have found that strong differences remain after
controlling for major lifestyle risk factors.16,18,26-29

There are some serious limitations in the samples of most prior prospective
studies on the contribution of health risk behaviors to socioeconomic differences
in mortality. Although population-based samples were used, the populations
were generally confined to a limited geographic area, such as a single city,
county, or small region of a country, and, in many cases, samples were further
restricted by including only males.16,18,20,26-29
In addition, much previous work has not provided a careful analysis of 2 primary
socioeconomic indicators—education and income—even though it is
quite possible that the mechanisms by which income and education are related
to health behaviors and/or mortality differ significantly.

The degree to which health behaviors explain or mediate the influence
of socioeconomic factors on mortality has important ramifications for health
policy. The research presented here attempts to bring greater clarity to this
issue by addressing the following questions: (1) what is the relationship
between the socioeconomic factors of education and income and health behaviors,
such as cigarette smoking, body weight, consumption of alcoholic beverages,
and physical activity; (2) what are the relative magnitudes of the effects
of education, income, and health behaviors on all-cause mortality; and (3)
to what extent do health behaviors explain education and income differences
in mortality, and does this vary by age, race, or sex? Our approach uses a
nationally representative, longitudinal sample that includes both men and
women, considers the effects of income and education separately, and investigates
demographic subgroup variation in the relationship between education, income,
health behaviors, and mortality.

Methods

Study Design and Sample

The data analyzed for this study are from the Americans' Changing Lives
(ACL) longitudinal survey conducted by the University of Michigan Survey Research
Center. A stratified, multistage area sample of noninstitutionalized persons
25 years of age or older living in the coterminous United States was selected
for study over time. Persons aged 60 years and older and blacks were oversampled.
Initial face-to-face interviews were conducted with 3617 persons in 1986,
representing 70% of all sampled households and 68% of sampled individuals.
Information on the independent variables being studied (as described below)
was taken from the 1986 ACL wave 1 survey. Two subsequent waves were conducted
in 1989 and 1994. Additional details on the ACL survey design and methods
are provided elsewhere.12,30

Information on deaths among sample respondents from mid-1986 through
March 1994 was obtained from informants and through the National Death Index.
The main outcome variable is all-cause mortality. In addition, underlying
causes of death (obtained from death certificates) were grouped into 4 categories
based on the International Statistical Classification of
Diseases, 10th Revision (ICD-10): (1) tumors,
(2) cardiovascular diseases, (3) all other diseases, and (4) external causes,
such as unintentional injury, suicide, homicide, or legal intervention. To
date, 90.3% of all deaths have been verified with death certificates. Reports
of the 9.7% of deaths (n=53) not yet verified with death certificates were
reviewed carefully, and actual death appears to be certain in each case. For
these cases, the month and year of death were ascertained from information
about the deaths obtained from informants.

Socioeconomic Factors and Other Sociodemographic Measures

The socioeconomic factors being studied are education and income, based
on self-reported information from the ACL wave 1 survey. Education is measured
as respondents' total years of completed education and is grouped as a 3-category
classification: 0 through 11 years; 12 through 15 years; and 16 or more years.
Income is measured as the combined income from all sources of the respondent
and his or her spouse in the preceding year, and also is grouped into 3 categories:
$0 through $9999; $10000 through $29999; and $30000 or more. More refined
categories of education and income produced similar results for the analyses
presented, as did adding controls for household size and assets.

Age is grouped into 6 categories: 25 through 34 years; 35 through 44
years; 45 through 54 years; 55 through 64 years; 65 through 74 years; and
75 years or older. Other sociodemographic variables being studied include
sex (male vs female), race (nonblack vs black), and urbanicity of residence
(central city, suburban, or rural). Previous research has found these demographic
variables to be related to socioeconomic factors, health risk behaviors, and
mortality. Thus, they are included in the analysis primarily as controls for
potential confounders.

Behavioral Risk Factor Measures

Health behavior indicators are based on self-reported information from
respondents at ACL wave 1. Cigarette smoking is coded as never smoked, former
smokers, and current smokers. Alcohol drinking is coded using 3 categories
based on the number of drinks consumed in the past month: nondrinkers (0 alcoholic
drinks in past month), moderate drinkers (1-89 drinks), and heavy drinkers
(≥90 drinks). Body weight was measured using the body mass index (BMI),
weight in kilograms divided by the square of height in meters, based on self-reported
weight and height. The body weight variable was coded as normal body weight,
overweight, and underweight. Following the methods of Berkman and Breslow,1 those in the highest 15% of the weighted sex-specific
BMI distributions were coded as overweight and those in the lowest 5% of the
weighted sex-specific BMI distributions were coded as underweight.

A physical activity index was computed based on answers to questions
regarding how often the respondent engaged in active sports or exercise, gardening
or yard work, and taking walks. Physical activity index scores were divided
into quintiles to create 5 groupings of near-equal sample size. The group
in the top quintile represents the 21% of the weighted sample that is the
most physically active.

Health Status

Three variables were available to measure baseline health status: (1)
self-rated health measured with a single 5-category scale classified as excellent,
very good, good, fair, and poor; (2) the number of major chronic conditions
experienced in the last year from a list of 10 conditions; and (3) an index
of functional status, with the lowest score of 1 representing confinement
to a chair or bed and the highest score of 4 representing the ability to do
heavy work inside or outside the house.30

Statistical Analysis

In all analyses, the data were weighted to adjust for differential response
rates and variation in probabilities of selection into the sample. Poststratification
weights adjust ACL wave 1 sample results to the July 1, 1986, Bureau of the
Census population estimates by sex, age, and region of the country. Descriptive
statistics were obtained through the Statistical Analysis System, SAS Institute,
Inc, Cary, NC, including frequency distributions of all variables being studied,
cross tabulations of the socioeconomic variables and health risk behaviors,
and cross tabulations of socioeconomic variables and mortality. In creating
contingency tables regarding the relationship between socioeconomic factors
and health risk behaviors, direct standardization to the age distribution
of the weighted ACL wave 1 population was used to account for the strong association
between age and socioeconomic factors.31

The Cox proportional hazards model was used to estimate the relative
risk of mortality in terms of various background, socioeconomic, and health
behavior variables. Taylor series linearization procedures using SUDAAN, Research
Triangle Institute, Research Triangle Park, NC, were used to make adjustments
to standard errors for the complex sample design. The effects of each independent
variable being studied on mortality were analyzed separately. A series of
multiple predictor models were then estimated. First, the relative hazard
rate of mortality was estimated for income and education groups both separately
and together, controlling for age, sex, race, and urbanicity. Second, the
behavioral risk factors being studied were added to the base model to investigate
how much of the socioeconomic differentials in mortality could be attributed
to these factors. Models were also run in which controls for baseline health
status were added and in which possible interactive effects between health
behaviors and variables such as education, income, sex, and race were explored.

Results

A significant portion of sample respondents (representing the national
population) were socioeconomically disadvantaged (Table 1). A total of 25.6% of the weighted sample reported 0 to
11 years of education, and 19.2% reported annual incomes of less than $10000
at ACL wave 1. A total of 546 respondents (15.1% of the overall sample and
9.9% of the weighted sample) died during the 7.5-year follow-up period. The
deaths included 255 males and 291 females, 338 nonblacks and 208 blacks, and
147 persons younger than 65 years and 399 persons aged 65 years and older.

The distribution of the 4 behavioral risk factors being studied significantly
varied by educational attainment and annual household income, adjusting for
age (Table 2). For example, persons
with the least amount of education and with the lowest incomes were significantly
more likely to be current smokers, overweight, and in the lowest quintile
for physical activity. Additional analyses suggest that there was a high degree
of stability in individuals' health behaviors across ACL study waves. For
example, of those who were overweight at wave 1, 84% were overweight at wave
2, and of those who were current smokers at wave 1, 79% were still smoking
at wave 2.

Table 3 presents the hazard
rate ratios of mortality by education and income for males and females separately.
Those with low educational attainment were significantly more likely to die
than those with 16 or more years of education. The relationship between education
and mortality and between income and mortality was stronger for females. Both
men and women in the lowest-income category were more than 3 times as likely
to die during the follow-up period of the study than those in the highest
group, controlling for age and other sociodemographic variables (Table 3). While education was strongly
related to health behaviors, income was more predictive of mortality than
education.

The relationship between socioeconomic factors, health behaviors, and
mortality was explored by conducting a sequence of Cox proportional hazards
models. The results of a model including statistical controls for age, race,
urbanicity, sex, education, and income are presented as model 1 in Table 4. The results show that the effect
of income on mortality was strong and significant when controlling for educational
attainment and background demographic variables. However, when these sociodemographic
variables were considered simultaneously, the bivariate effect of education
on mortality attenuated to a statistically insignificant level. Additional
model testing (results not shown) demonstrated that the mechanism by which
education was related to mortality was through its association with income.

When the 4 health behaviors being studied were added individually to
model 1 (results not shown), the effect of income on mortality attenuated
slightly yet remained significant for both the lowest-income and the middle-income
groups. For example, when physical activity was added to the model, the coefficient
for the effect of income attenuated a small amount, suggesting that physical
activity explains only a small proportion of the relationship between income
and mortality. The results of the full model when all health behaviors were
considered simultaneously (model 2, Table
4) show that there was still a strong and significant income effect
on mortality for both the middle-income (odds ratio [OR]=2.14; CI, 1.38-3.25)
and the low-income groups (OR=2.77; CI, 1.74-4.42). The 4 health behaviors
together accounted for 12% to 13% of the predictive effect of income on mortality.

In terms of the health behaviors, the results suggest that being severely
underweight or having lower levels of physical activity were significant risk
factors for subsequent mortality, controlling for demographic and socioeconomic
characteristics (Table 4). The
relationship between physical activity and mortality appeared to be monotonic,
suggesting that there are gains not only from being physically active but
also from increasing amounts of activity. In regard to being underweight,
descriptive information on the severely underweight individuals who died shows
that the majority (78%) were age 75 years or older. Notably, the effects of
smoking and drinking were no longer significant once they were adjusted for
the demographic, socioeconomic, and other health behavior variables, and being
overweight was not significant in any of the models.

It is plausible that baseline differences in both income and health
behaviors reflect differences in health status to some degree. The 3 ACL wave
1 health status variables (self-reported health, number of chronic conditions,
and functional status) were added separately and simultaneously to a model
controlling for background characteristics, income, education, and health
behaviors. The results (not shown) do not suggest any different patterns or
effects from those shown in Table 4.
The relationship between income and mortality remained strong and significant
(P<.001) controlling for baseline health status
and health behaviors simultaneously.

Additional analyses, including an examination of interaction tests,
were conducted to see if the patterns and results observed for the full sample
were the same across subpopulations of interest. Six subgroups were examined:
males, females, nonblacks, blacks, persons ages 25 through 64 years, and persons
ages 65 years and older. The results (not shown) did not reveal findings that
were substantially different from those for the total sample. Overall, health
behaviors explained only a small proportion of income differences in mortality
across sex, race, and age groups.

For those descendents with death certificate information (n=493), the
weighted underlying cause of death was tumors, 30%, cardiovascular disease,
28%, other diseases, 37%, and external causes, 5%. Controlling for income
and other sociodemographic variables, education was not significantly related
to any cause-of-death category. Those in the lowest-income group had significantly
higher rates of tumor deaths and cardiovascular disease deaths, and those
in the middle-income group had a significantly higher rate of tumor deaths.
Several health behaviors were associated with a significantly higher risk
of death in specific categories (ie, both current and former smoking was associated
with an increased risk of tumor deaths, heavy drinking was associated with
increased risk of death from external causes, and low physical activity was
associated with increased risk of tumor and cardiovascular deaths). However,
for both tumor and cardiovascular disease deaths separately, controlling for
health behaviors attenuated the association between low and moderate income
with mortality to the same degree observed for death from all causes. The
income effects decreased by 12% to 17% when health risk behaviors were added
to the models, similar to what was observed in analyses where all causes of
death were grouped together.

Comment

The ACL survey findings show that lower levels of education and income
are associated with a significantly higher prevalence of health risk behaviors,
including smoking, being overweight, and physical inactivity. The results
also show that lower income (net of demographic characteristics) leads to
a significant increase in mortality risk, yet the influence of major health
risk behaviors explains only a modest proportion of this relationship.

Our findings of strong socioeconomic differences in mortality (including
larger socioeconomic differentials for women than men, and a stronger mortality
effect for income than for education for both women and men) are consistent
with previous longitudinal research.13-18
In addition, our findings regarding the association between socioeconomic
factors, health behaviors, and mortality are similar to previous studies conducted
using limited samples. For example, in a 20-year study of Ontario males, Hirdes
and Forbes6 concluded that smoking and other
health practices are not the primary mechanisms linking socioeconomic status
and mortality. Similarly, the Alameda County Study28
showed that the risk of mortality associated with living in high-poverty areas
of Oakland, Calif, changed little after adjusting for smoking, alcohol consumption,
physical activity, BMI, and sleep patterns. Our results contribute to previous
studies by providing evidence regarding the association between education,
income, health behaviors, and mortality from a nationally representative sample
that includes both men and women.

While there appears to be little debate regarding the need to improve
the health of populations with low levels of income and education, the appropriate
focus of policy and program responses is less clear. An important area on
which both policy rhetoric and action have focused is that of health education
and health promotion at the individual level. A tacit assumption among some
policymakers and health authorities is that an important way to reduce socioeconomic
gaps in health status is to improve the health behaviors among those with
low levels of income and education. This position is obvious in the Department
of Health and Human Services' Healthy People 2000: National
Health Promotion and Disease Prevention Objectives and other reports
on the state of health among poor and minority persons in the United States.2,3,23-25
This position has also been articulated in the lay press. For example, an
opinion piece in the Wall Street Journal32 criticized public health researchers' growing focus
on social systems and institutions, arguing that poor people tend to have
worse health and shorter life expectancies, primarily "because unhealthy habits
are more prevalent on the lower rungs of the socioeconomic ladder."

Our results suggest that despite the presence of significant socioeconomic
differentials in health behaviors, these differences account for only a modest
proportion of social inequalities in overall mortality. Thus, public health
policies and interventions that exclusively focus on individual risk behaviors
have limited potential for reducing socioeconomic disparities in mortality.
While reducing the prevalence of behavioral risk factors is an important and
critical public health goal, socioeconomic differentials in mortality are
due to a wider array of factors and, therefore, would persist even with improved
health behaviors. Increasing health promotion and disease prevention efforts
among the disadvantaged is not a "magic policy bullet" for reducing persistent
socioeconomic disparities in mortality.

If health risk behaviors do not explain much of the relationship between
socioeconomic factors and mortality, what else can account for this strong
association? First, differences in exposure to occupational and environmental
health hazards across social strata do exist and, thus, may be playing a role
in mortality inequalities.33-35
Second, although not a panacea for eliminating socioeconomic differences in
health status, improved equity regarding access to and use of preventive and
appropriate therapeutic medical care is viewed as having some potential for
preventing the further deterioration of health in disadvantaged populations.8,23,25,36-40

Third, socioeconomic stratification itself may be a social force that
has deleterious health effects for those in the lower strata. As Blane41 explains, socioeconomic inequalities in societies
"structure the life experiences of their members so that advantages and disadvantages
tend to cluster cross-sectionally and accumulate longitudinally." Persons
in lower socioeconomic strata have increased exposure to a broad range of
psychosocial variables predictive of morbidity and mortality. This includes
(1) a lack of social relationships and social supports; (2) personality dispositions,
such as a lost sense of mastery, optimism, sense of control, and self-esteem
or heightened levels of anger and hostility; and (3) chronic and acute stress
in life and work, including the stress of racism, classism, and other phenomena
related to the social distribution of power and resources.25,30,34,42-45
Furthermore, Lynch et al46 report that both
the psychosocial orientations and health risk behaviors of adults are more
common among those whose parents were poor when they were children. Thus,
many individual characteristics, such as personality factors, psychosocial
attitudes and orientations, and health risk behaviors, should be viewed as
products of or responses to social environments (eg, family, school, neighborhood,
cultural context, etc) rather than strictly as individual behavioral choices.47

There are a number of limitations in our study methods. First, the health
behaviors being investigated were self-reported and were not assessed retrospectively.
Literature on the accuracy of self-reported health behaviors suggests that,
although most people report honestly for behaviors that are not illegal, the
biases that do exist are in the direction of underreporting negative health
behaviors.48-50
Thus, the result of any problems in the reporting of health behaviors would
likely be an underestimation of their effects. Second, the length of the follow-up
period in this prospective study limits our ability to investigate the longer-term
effects of income, education, and health behaviors on mortality. Third, the
small number of deaths for some of the demographic groups puts limits on the
multivariate subgroup analysis that could be performed. Fourth, it is possible
that additional health behaviors and risk factors not studied explain more
of the relationship between income and mortality. Lynch et al26
report that, in a longitudinal study of Finnish men, the association between
socioeconomic status and mortality from all causes and from cardiovascular
disease was eliminated by simultaneous adjustment for biologic factors, psychosocial
factors, and health risk behaviors. A full explanation of social inequalities
in mortality, however, needs to address why all of these risk factors tend
to be patterned by socioeconomic characteristics.

Our results suggest that both health behaviors and socioeconomic factors
are important determinants of mortality. While health behaviors are related
to both income and education, they account for a small proportion of observed
socioeconomic differences in mortality. Thus, the problem of lifestyle and
mortality is not just one of inadequate education or income, and the problem
of socioeconomic differentials in mortality is not just a problem of lifestyle
choices. We must look to a broader range of explanatory risk factors, including
structural elements of inequality in our society.

Salonen JT. Socioeconomic status and risk of cancer, cerebral stroke and death
due to coronary heart disease and any disease: a longitudinal study in eastern
Finland. J Epidemiol Community Health.1982;36:294-297.Google Scholar